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Specialized accelerators for tensor-operations, such as blocked-matrixoperations and multi-dimensional convolutions, have been emerged as powerfularchitecture choices for high-performance Deep-Learning computing. The rapiddevelopment of frameworks, models, and precision options challenges theadaptability of such tensor-accelerators since the adaptation to newrequirements incurs significant engineering costs. Programmable tensoraccelerators offer a promising alternative by allowing reconfiguration of avirtual architecture that overlays on top of the physical FPGA configurablefabric. We propose an overlay ({\tau}-VTA) and an optimization method guided byagile-inspired auto-tuning techniques. We achieve higher performance and fasterconvergence than state-of-art.

Agile Autotuning of a Transprecision Tensor Accelerator Overlay for TVM Compiler Stack